Exploratory Data Analysis (EDA) and Data Visualization from CSV and excel file

1. Data Loading and Preprocessing: Load the dataset from both CSV and Excel files using Pandas. Display the first 5 rows and basic dataset info (.head(), .info(), .describe()). Check for missing values and handle them (drop, fill, or impute). 2. Data Cleaning and Feature Engineering : Identify duplicate rows and remove them. Convert categorical variables into numerical if needed (using LabelEncoder or OneHotEncoding). Normalize/scale numerical features if necessary. 3. Exploratory Data Analysis (EDA) : Check basic statistics: Mean, median, mode, standard deviation. Visualize distributions: Use histograms, boxplots, and KDE plots. Identify correlations: Use a heatmap to display relationships between variables. Outlier Detection: Use boxplots or scatter plots. 4. Data Visualization: Univariate Analysis: Plot bar charts, histograms, and pie charts to analyze individual features. Bivariate Analysis: Create scatter plots, pair plots, or violin plots to explore relationships. Multivariate Analysis: Use heatmaps and correlation matrices to identify patterns. Custom Visualization: Provide one insightful visualization of your choice.